v6d

We recently noticed some strange email notifications from Github with content "Merged #948 into main.", which told that pull requests on our repo has been merged by stranger who actually doesn't have the write permission to our repo! After further inspection, we found that the merge event is triggered by the creator of the pull request pushing current main commits to the PR's "from" branch. Moreover, when pushing current main to a pull request, - The pull request is displayed as "Merged" - A "PR merged into main" email is sent to all subscribers (mainly the repo owners) - A "PR merged" contribution is displayed on the creator's Github profile Closing dangling pull requests is a quite resonable design, but mark it as "Merged" rather than "Closed" would confuse people to let them think they are hacked at the first glance (note that there ev

Thanks for you interests on GraphScope! We do have a concrete plan for k8s-less deployment and we already have an issue [1] to track that. That will be available before the end of March 2021. To simplify the environment setup process we will release a docker image for end-users, but without docker will be ok as well (requires building from sources). GraphScope use vineyard [2] as the storage layer for im-memory graph data structures. And current the graph type (aka. ArrowPropertyFragment in GraphScope) uses a set of arrow tables and arrays under the hood. GraphScope supports a `to_vineyard_dataframe` method on the computation context [3]. We also has a plan for integration between vineyard and dask (may could be delivered in March as well). At that time the interop between dask would be straightforward. [1]: https://github.com/alibaba/Gra

alibaba/euler is a lightweight library built specifically for GNN sampling only. While GraphScope is built for many kinds of graph tasks such as gremlin, graph analytics and GNN sampling. You can check this example to get an idea what GraphScope can do. https://nbviewer.jupyter.org/github/alibaba/GraphScope/blob/... The graphs on GraphScope is backed by vineyard (https://github.com/alibaba/libvineyard). And that enables GraphScope to have multiple specifically optimized runtimes (written in C++, rust and Python) for different tasks shares the distributed graph data in memory efficiently.

> if you have a small graph would it unreasonable to use graphscope to analyze it - what if it was a small graph that you expected to grow big later? You are right. GraphScope is built for processing very large graphs, but for small graphs, it should work just as well. If you expect your graphs to grow too big to be handled on a single machine in the future, I think it is a good idea to start with GraphScope even when it is small. Stay tuned, we got plans to make GraphScope nicer to use for smaller graphs without a k8s cluster. > What if you have a graph inside a graph db - I guess GraphScope gives you extra performance by allowing you to distribute processing for tasks that would make sense - any other benefits like that? Besides extra performance, there could a few other benefits depending on your scenario. 1. Many graph analytical / GN

Vineyard is a distributed in-memory data manager. Features: 1. distributed in-memory immutable data storage; 2. zero-copy data access through shared-memory; 3. out-of-the-box high-level data abstractions for commonly used data structures; 4. pre-built drivers for I/O, migration, checkpoint, etc; 5. C++ and Python API; and 6. Kubernetes-integration for large-scale big data applications Github: https://github.com/alibaba/libvineyard (stars are welcomed!) Documentation: https://v6d.io Helm charts: https://artifacthub.io/packages/helm/vineyard/vineyard Any comments and contributions from the community are welcomed!